The feature attraction

Bing Xue uses evolutionary computation to help us make sense of the larger picture in areas such as biomedical sciences and aquaculture.

How did an undergraduate degree in management science lead to a fully-fledged academic and research career in the niche field of evolutionary computation? Apparently, that’s what a chance meeting with data mining can do.

“I took a paper in data mining as part of the management science major and fell in love with it,” says Associate Professor Bing Xue from Te Herenga Waka—Victoria University of Wellington’s School of Engineering and Computer Science. “Data mining is all about finding patterns from huge datasets and I enjoy trying to make sense of the larger picture."

“I also enjoyed the programming side, which includes more of the ‘hard skills’. I believe this gives you more control of your work and is another key reason why I chose to go down the computer science stream.”

Having completed her undergraduate degree in Management Science at China’s Henan University of Economics and Law in Zhengzhou and Master’s degree in Engineering at the country’s Shenzhen University, Associate Professor Xue chose to pursue her PhD at Victoria University of Wellington, given her positive interactions with staff here as well as the University’ established reputation for high-quality research.

Making the move to a new country and starting afresh can be daunting and it was with some apprehension that Associate Professor Xue moved to Wellington in 2010. “I wouldn’t say things were challenging when I moved here, but they were definitely interesting. The work was similar to what I was already doing but I felt like everybody else knew much more than me. I wasn’t a native English speaker and it can be pretty intimidating, being in meetings with people who are so fluent and comfortable in the language.

“But the meetings made a huge difference—people want you to do well and they offer a lot of support. I remember a specific incident over one Christmas, when I was having a problem with my code. It wasn’t an issue and I could have written it later, but there was a group working on a conference deadline. One of them heard I was having trouble—and then they all came together to help me talk through the problem, to look at different ways to solve it.”

The increasingly ubiquitous nature of artificial intelligence (AI) piqued Associate Professor Xue’s interest in this area at a young age. “AI is everywhere today and it continues to evolve into an integral part of our daily routines. And I find that very intriguing and inspiring—the ability of technology to become such a seamless part of our lives.

“Given feature selection, the area of my research, didn’t have strong foundations at that time, it was a great opportunity for me to learn. Obviously, a PhD is a huge commitment and I remember often feeling like I was working extremely long hours. One thing I spent a lot of time on when I started here was writing in English. Not being my first language, this was a bit of a challenge. I had a lot of support from my supervisor—he helped me with the first conference paper I submitted and although that wasn’t accepted I learnt a lot in terms of what was expected.”

Associate Professor Xue joined the staff of the School of Engineering and Computer Science after an 18-month stint as a postdoctoral researcher. The University’s focus on teaching excellence, alongside its emphasis on research, was one of the main reasons she chose to stay on after she completed her studies.

Associate Professor Xue won her first grant from the Marsden Fund to support research excellence in 2016, for a project focused on high-dimensional classification. Classification is a basic task and applies to various areas ranging from medical diagnosis to financial fraud identification. It categorises instances into classes (for example, bank loan applicants into safe or risky) based on features (income, age, occupation, etc). However, many classification tasks have a large number of features/dimensions, which causes the “curse of dimensionality” and leads to low accuracy. Associate Professor Xue’s project proposed evolutionary computation algorithms for feature selection to improve classification accuracy by reducing the number of features and substantially shortening the computation time.

She won her second Marsden grant in 2019 for her proposal on new evolutionary computation-based approaches to automated design of deep convolutional neural networks (DCNNs). The most-widely studied deep learning methods, DCNNs are primarily used for solving challenging image and vision tasks. Associate Professor Xue’s project aims to improve accuracy in image classification, which is challenging because the difficulty involved in designing DCNNs often leads to low accuracy and slow processing. The proposed new methods are expected to automatically design and learn DCNN architectures that significantly improve classification accuracy.

“I enjoy both the teaching and research side of my work,” says Associate Professor Xue. “Receiving two Marsden grants is definitely one of the main highlights of my career. Having said that, to me the experience of putting together a lecture and knowing students draw a lot of value from it is equally important. Seven years ago, I decided this was the best place for my academic career—and I still believe that.”

Having lived here for a decade and now a new mother, Associate Professor Xue thinks of Wellington as home. “This is a great place to live and study in. It’s very safe and you get to meet people from various cultures—the kind of life experience you get from living here is unique.”

In a nutshell

My research matters because… Artificial intelligence (AI) is everywhere today and it continues to evolve into an integral part of our daily routines.

One of the inspirations for my research has been… The idea AI can make our lives so much simpler.

The best thing about my job is… That I get to combine my two passions—research and teaching.

My career highlight so far has been… Receiving two Marsden grants for research projects in AI.

My advice to aspiring researchers is… Keep going and don't be afraid of failures along the way. All the effort you put in will result in rewards at some point.